Amyloid load in fat tissue reflects disease severity and predicts survival in amyloidosis
Bibliographic record
Abstract
OBJECTIVE: The severity of systemic amyloidosis is thought to be related to the extent of amyloid deposition. We studied whether amyloid load in fat tissue reflects disease severity and predicts survival. METHODS: We studied all consecutive patients with systemic amyloidosis seen between January 1994 and January 2007 in our tertiary referral center. Congo red-stained abdominal fat smears were graded by 2 observers using a validated semiquantitative scoring system. Disease severity was measured by the total number of major organs involved and the extravascular retention of the serum amyloid P component (EVR(24)). The association of amyloid load in fat tissue with disease severity and overall survival was studied using multiple regression analysis. RESULTS: Two hundred twenty patients were included in the study (120 with AL amyloidosis, 66 with AA amyloidosis, and 34 with ATTR amyloidosis). Amyloid grade in fat tissue was associated with the number of major organs involved and EVR(24). Female sex turned out to be associated with a higher grade of amyloid in fat tissue than male sex. Amyloid grade in fat tissue was an independent predictor of decreased survival, as were heart involvement, the number of organs involved, AA or AL type of amyloid, and age. CONCLUSION: The amount of amyloid in subcutaneous fat tissue in systemic amyloidosis reflects disease severity, as measured by the number of organs involved and EVR(24), and predicts decreased survival independent of other well-known factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".